Functional mechanism : regression analysis under differential privacy
ɛ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however,...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2014
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| Online Access: | https://hdl.handle.net/10356/102390 http://hdl.handle.net/10220/18929 http://arxiv.org/abs/1208.0219 |
| _version_ | 1826120788021870592 |
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| author | Winslett, Marianne Zhang, Jun Zhang, Zhenjie Xiao, Xiaokui Yang, Yin |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Winslett, Marianne Zhang, Jun Zhang, Zhenjie Xiao, Xiaokui Yang, Yin |
| author_sort | Winslett, Marianne |
| collection | NTU |
| description | ɛ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. As case studies, we apply the functional mechanism to address two most widely used regression models, namely, linear regression and logistic regression. Both theoretical analysis and thorough experimental evaluations show that the functional mechanism is highly effective and efficient, and it significantly outperforms existing solutions. |
| first_indexed | 2024-10-01T05:22:11Z |
| format | Journal Article |
| id | ntu-10356/102390 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:22:11Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/1023902020-05-28T07:17:46Z Functional mechanism : regression analysis under differential privacy Winslett, Marianne Zhang, Jun Zhang, Zhenjie Xiao, Xiaokui Yang, Yin School of Computer Engineering DRNTU::Engineering::Computer science and engineering ɛ-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. As case studies, we apply the functional mechanism to address two most widely used regression models, namely, linear regression and logistic regression. Both theoretical analysis and thorough experimental evaluations show that the functional mechanism is highly effective and efficient, and it significantly outperforms existing solutions. 2014-03-20T08:56:42Z 2019-12-06T20:54:14Z 2014-03-20T08:56:42Z 2019-12-06T20:54:14Z 2012 2012 Journal Article Zhang, J., Zhang, Z., Xiao X., Yang, Y., & Winslett, M. (2012). Functional mechanism : regression analysis under differential privacy. Proceedings of the VLDB Endowment, 5(11), 1364-1375. https://hdl.handle.net/10356/102390 http://hdl.handle.net/10220/18929 http://arxiv.org/abs/1208.0219 en Proceedings of the VLDB endowment © 2012 VLDB Endowment. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Winslett, Marianne Zhang, Jun Zhang, Zhenjie Xiao, Xiaokui Yang, Yin Functional mechanism : regression analysis under differential privacy |
| title | Functional mechanism : regression analysis under differential privacy |
| title_full | Functional mechanism : regression analysis under differential privacy |
| title_fullStr | Functional mechanism : regression analysis under differential privacy |
| title_full_unstemmed | Functional mechanism : regression analysis under differential privacy |
| title_short | Functional mechanism : regression analysis under differential privacy |
| title_sort | functional mechanism regression analysis under differential privacy |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/102390 http://hdl.handle.net/10220/18929 http://arxiv.org/abs/1208.0219 |
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